def main():
args = parseArgs()
finetune = args.finetune
print finetune
experimentName = 'MyVae_featureGeneration_reconstructionClassification'
if not os.path.exists(workingDir):
os.mkdir(workingDir)
experimentDir = os.path.join(workingDir, experimentName)
if not os.path.exists(experimentDir):
os.mkdir(experimentDir)
#finetune = False
mConfig = getConfig(experimentName, finetune)
mConfig.workingDir = experimentDir
mData = ChaLearn(mConfig.dataPath, 1)
batchSource_train, batchSource_test, batchSource_valid, segLen_max, sampleDim, frameLabelList_train, frameLabelList_test, frameLabelList_valid = mData.getData_vaeReconstruction(mConfig)
mNet = VaeBlstm_v5(sampleDim, segLen_max, mConfig)
mNet.train(6000, mConfig.batchSize_train, batchSource_train, mConfig.batchSize_test, batchSource_test)
pass
def main():
experimentName = 'StandardVae_featureGeneration_zClassification'
args = parseArgs()
finetune = args.finetune
mConfig = getConfig(experimentName, finetune)
dataset = 'chalearn'
experimentDir = os.path.join(workingDir, experimentName)
if not os.path.exists(experimentDir):
os.mkdir(experimentDir)
mConfig.workingDir = experimentDir
mData = ChaLearn(mConfig.dataPath, 1)
batchSource_train, batchSource_test, batchSource_valid, segLen_max, sampleDim, frameLabelList_train, frameLabelList_test, frameLabelList_valid = mData.getData_vaeReconstruction(mConfig)
mNet = VaeBlstm_v14(sampleDim, segLen_max, mConfig)
mNet.train(6000, mConfig.batchSize_train, batchSource_train, mConfig.batchSize_test, batchSource_test)
def main():
experimentName = 'Blstm_rawJoint'
mConfig = getConfig(experimentName, 'False')
dataset = 'chalearn'
#workingDir = '/wrk/hshi/DONOTREMOVE/git/FeatureLearningAndGestureRecognition/ExperimentArchive/New/20180418'
experimentDir = os.path.join(workingDir, experimentName)
if not os.path.exists(experimentDir):
os.mkdir(experimentDir)
mConfig.workingDir = experimentDir
mData = ChaLearn(mConfig.dataPath, 1)
batchSource_train, batchSource_test, batchSource_valid, segLen_max, sampleDim, frameLabelList_train, frameLabelList_test, frameLabelList_valid = mData.getData(mConfig)
mNet = Blstm_v1(sampleDim, segLen_max, mConfig)
mNet.train(6000, mConfig.batchSize_train, batchSource_train, mConfig.batchSize_test, batchSource_test)
def main():
experimentName = 'StandardVae_selfReconstruction_rawJoint_zClassification'
dataset = 'chalearn'
#workingDir = '/wrk/hshi/DONOTREMOVE/git/FeatureLearningAndGestureRecognition/ExperimentArchive/New/20180418'
experimentDir = os.path.join(workingDir, experimentName)
if not os.path.exists(experimentDir):
os.mkdir(experimentDir)
args = parseArgs()
finetune = args.finetune
mConfig = getConfig(experimentName, finetune)
mConfig.workingDir = experimentDir
mData = ChaLearn(mConfig.dataPath, 1)
batchSource_train, batchSource_test, batchSource_valid, segLen_max, sampleDim, frameLabelList_train, frameLabelList_test, frameLabelList_valid = mData.getData(mConfig)
mNet = VaeBlstm_v11(sampleDim, segLen_max, mConfig)
mNet.train(6000, mConfig.batchSize_train, batchSource_train, mConfig.batchSize_test, batchSource_test)
